Computed Tomography Colonography (CTC) is a procedure whereby an accurate picture of the colon can be obtained from X-ray scan data. Images obtained by CTC are utilised to screen the colon for polyps or other abnormalities that could indicate a patient's risk to colon cancer or other colon diseases. CTC represents an increasingly popular alternative to traditional colonoscopy methods due to the minimally invasive nature of CTC. However, from a patient's point of view, one of the limitations of CTC is a requirement for rigorous bowel cleansing and preparation prior to scanning. Bowel cleansing is necessary to remove stool and other residual materials and fluids that could potentially obscure important features in the scan. The perceived inconvenience and discomfort associated with bowel preparation may act as a deterrent to an otherwise minimally invasive method of examining the colon.
Preparations for electronic cleansing provide a reliable and welcome alternative to conventional bowel cleansing. Prior to the CTC scan, the patient ingests an X-ray opaque contrast agent, which tags colonic fluids and stool. Methods of electronic cleansing aim to identify and remove the tagged materials from a 3-dimensional data set by electronic (post-acquisition) means. Thus, electronic cleansing provides a means of virtually removing stool and other fluids that could potentially obscure important features in the scan. As such, electronic cleansing of tagged material from the CTC data set represents a viable alternative to traditional physical cleansing. A number of prior art documents directed to electronic cleansing are discussed below.
Ordinarily, a CTC scan will assign an intensity value to a particular type of voxel (or 3-dim volume element), e.g. air will have a different intensity value to colon tissue, which will in turn have a different intensity value to tagged material. Simply classifying a voxel based on the range in which the intensity value of the voxel falls is complicated by Partial Volume effects and soft tissue erosion discussed below. U.S. Pat. Nos. 6,331,116, 6,343,936 and 6,514,082 discuss a method of electronic cleansing comprising classifying the data into voxel type on the basis of intensity values, similarity data, probability functions and feature vector analysis. High-level feature extraction is utilised to remove undesired tagged materials. Classification of voxels in a CTC data image based on intensity value is also disclosed in U.S. Pat. No. 6,477,401. Tagged material is edge expanded proportional to the intensity values of adjacent voxels and is ultimately removed.
Summers et al. (AJR, 2005, 184, 105-108) describe a segmentation algorithm utilising a very low threshold value for colonic fluid/tagged materials. To minimise leakage of tagged material into adjacent structures the air-fluid boundary is not permitted to exceed 2 voxels and an air region cannot be below a fluid region. By calculating the mean and standard deviation of the fluid intensity values a modified fluid threshold is determined, and a second segmentation procedure is performed to minimise leakage. Franaszek and Summers have also developed a more advanced hybrid segmentation algorithm (IEEE Trans. Med. Imaging, 2006, 25(3), 358-368). The method employs techniques such as region growing, fuzzy connectedness and Laplacian level set segmentation to improve the accuracy of the segmentation process.
A disadvantage associated with the processing of tagged CTC images is that at the boundary of different regions, e.g. air voxels and tagged material voxels, there are voxels whose intensities do not correspond to the intensity of either region and as such can be erroneously classified. These are referred to as partial volume voxels, and can comprise several layers of voxels. Methods addressing the problems associated with the incorrect classification of partial volume voxels are discussed in the prior art and a select few are communicated below.
Chen et al. (IEEE Trans. Med. Imaging, 2000, 19(12), 1220-1226) describe a segmentation approach to addressing the partial volume effect. Lakare et al. (IEEE Visualization, 2000, 37-44) developed a segmentation ray method comprising casting rays through the data set and scanning them for characteristic profiles that might indicate an air/tagged material interface. A statistical approach to partial-volume image segmentation utilising an expectation-maximization algorithm has been disclosed by Wang et al. (IEEE Trans Biomed Eng. 2006, 53(8), 1635-1646). A similar approach is communicated in WO 2007/064980 to account for the possibility that volume elements are capable of representing more than one material type. Statistical methods are utilised to generate a partial volume image model. Zalis et al. (IEEE Trans. Med. Imaging, 2004, 23(11), 1335-1343) communicated a method directed to addressing volume averaging artefacts that undesirably persist in a CTC scan once tagged fluid has been removed. Such volume averaging artefacts are due to air/tagged material partial volume voxels. Subtraction of this tagged material also results in a rapid transition in voxel intensity between boundary colonic tissue and subtracted intraluminal bowel contents and results in unwanted jagged edges in the processed image. The mucosal layer is reconstructed as a layer three pixels thick exhibiting a gradual decrease in voxel intensity from soft tissue to air. This publication is limited to mucosal reconstruction subsequent to over-subtraction of tagged material.
In addition to the problems associated with erroneous classification of partial volume voxels, removal of tagged material from a CTC data set can result in the erosion of soft-tissue structures partially covered by tagged colonic fluid. Incorrect removal of tagged material can lead to false positive polyp detection, or more seriously, missed polyps (false negative). Zalis and co-workers (Int J. CARS 2006, 1, 369-388; and WO2007/048091) further describe a method for the recovery of soft tissue structures submerged in or partially covered by tagged material utilising a morphological based approach. The method applies a Hessian operator matrix on a region of interest and the operator iteratively moves through the image. If the eigenvalues of the Hessian operator in a particular region of interest correspond to a predetermined signature of folds and/or polyps, the appropriate structure can be enhanced through mathematical operations prior to removal of the tagged bowel contents.
Notwithstanding the state of the art there remains a need for a method of electronic cleansing adapted to addressing the problems associated with erroneous removal of soft tissue structures submerged in or partially covered by tagged material. Furthermore, such a method would also be capable of circumventing the problems associated with mistaken classification on account of incorrect labelling of partial volume voxels.